Modelling daily reference evapotranspiration based on stacking hybridization of ANN with meta-heuristic algorithms under diverse agro-climatic conditions

Authors :Ahmed Elbeltagi, Nand Lal Kushwaha, Jitendra Rajput, Dinesh Kumar Vishwakarma, Luc Cimusa Kulimushi, Manish Kumar, Jingwen Zhang, Chaitanya B. Pande, Pandurang Choudhari, Sarita Gajbhiye Meshram, Kusum Pandey, Parveen Sihag, Navsal Kumar & Ismail Abd-Elaty

Abstract :

Precise estimation of reference evapotranspiration (ET0) is crucial for efficient agricultural water management, crop modelling, and irrigation scheduling. In recent years, the data-driven models using Artificial Intelligence (AI)-based meta-heuristics algorithms have gained the attention of researchers worldwide. In this study, we have investigated the performance of five AI-based models for ET0 estimation, including Artificial Neural Networks-Additive Regression (ANN-AR), ANN-Random Forest (ANN-RF), ANN-REPtree, ANN-M5Pruning Tree (ANN-M5P), and ANN-Bagging at New Delhi (i.e., semi-arid climate), and Srinagar (i.e., humid climate) stations and the best yielded algorithms were evaluated at the third station i.e. Ludhiana (i.e., sub-humid climate) located in Northern India. The performances indicators (i.e., Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Nash–Sutcliffe Efficiency (NSE), and Willmott Index (WI)) of hybrid meta-heuristics algorithms were compared to FAO-56 Penman–monteith (P-M FAO-56). Results revealed that the M5P algorithm under limited climate variables (i.e., Model 1, 2, and 3) and Bagging (Model 4 and 5) acted as efficient tools in optimizing the ANN structure. Therefore, the algorithm ANN-M5P predicted ET0 values precisely under models 1, 2, and 3. While the ANN-Bagging algorithms gave better ET0 estimation under models 4 and 5 for both the selected stations. The evaluation of best hybrid algorithms under each constructed model for the Ludhiana station showed that the ANN-M5P algorithm under Model-3 outperformed the other four models (MAE = 0.730 mm/day, RMSE = 0.959 mm/day, NSE = 0.779, and WI = 0.935). The present study demonstrated that the AI-based hybrid meta-heuristics algorithms (ANN-M5P and ANN-Bagging) are promising pathways for ET0 estimation